Development of Canonical Labeling System for Semi Structured Data Set

2009 ◽  
Vol 5 (2) ◽  
Author(s):  
Ford Lumban Gaol ◽  
Belawati H. Widjaja
Keyword(s):  
Structured Data ◽  
Data Set ◽  
Labeling System

scholarly journals A Framework for the Forensic Investigation of Unstructured Email Relationship Data

2011 ◽  
Vol 3 (3) ◽  
pp. 1-18 ◽  
Author(s):  
John Haggerty ◽  
Alexander J. Karran ◽  
David J. Lamb ◽  
Mark Taylor
Keyword(s):  
Forensic Analysis ◽  
Structured Data ◽  
Unstructured Data ◽  
Forensic Investigation ◽  
Qualitative Information ◽  
Data Set ◽  
Analysis Techniques ◽  
Potential Sources ◽  
Network Identification ◽  
Digital Investigation

The continued reliance on email communications ensures that it remains a major source of evidence during a digital investigation. Emails comprise both structured and unstructured data. Structured data provides qualitative information to the forensics examiner and is typically viewed through existing tools. Unstructured data is more complex as it comprises information associated with social networks, such as relationships within the network, identification of key actors and power relations, and there are currently no standardised tools for its forensic analysis. This paper posits a framework for the forensic investigation of email data. In particular, it focuses on the triage and analysis of unstructured data to identify key actors and relationships within an email network. This paper demonstrates the applicability of the approach by applying relevant stages of the framework to the Enron email corpus. The paper illustrates the advantage of triaging this data to identify (and discount) actors and potential sources of further evidence. It then applies social network analysis techniques to key actors within the data set. This paper posits that visualisation of unstructured data can greatly aid the examiner in their analysis of evidence discovered during an investigation.

Big Data and Analytics

2020 ◽  
pp. 47-65
Author(s):  
Sheik Abdullah A. ◽  
Priyadharshini P.
Keyword(s):  
Social Media ◽  
Big Data ◽  
Predictive Analytics ◽  
Data Warehousing ◽  
Big Data Analytics ◽  
Structured Data ◽  
Social Media Analytics ◽  
Data Set ◽  
Data Volume ◽  
Tools And Techniques

The term Big Data corresponds to a large dataset which is available in different forms of occurrence. In recent years, most of the organizations generate vast amounts of data in different forms which makes the context of volume, variety, velocity, and veracity. Big Data on the volume aspect is based on data set maintenance. The data volume goes to processing usual a database but cannot be handled by a traditional database. Big Data is stored among structured, unstructured, and semi-structured data. Big Data is used for programming, data warehousing, computational frameworks, quantitative aptitude and statistics, and business knowledge. Upon considering the analytics in the Big Data sector, predictive analytics and social media analytics are widely used for determining the pattern or trend which is about to happen. This chapter mainly deals with the tools and techniques that corresponds to big data analytics of various applications.

scholarly journals On Making of Micoquian Bifacial Backed Tools at Pietraszyn 49a, SW Poland

2020 ◽  
Author(s):  
Andrzej Wiśniewski ◽  
Marcin Chłoń ◽  
Marcel Weiss ◽  
Katarzyna Pyżewicz ◽  
Witold Migal
Keyword(s):  
Central Europe ◽  
Narrow Range ◽  
Comprehensive Analysis ◽  
Structured Data ◽  
The Other ◽  
Upper Palaeolithic ◽  
Data Set ◽  
Sw Poland ◽  
Marine Isotope Stage 3 ◽  
Early Upper

Abstract This paper attempts to show that manufacture of Micoquian bifacial backed tools was structured. Data for this study were collected using a comprehensive analysis of artefacts from the site Pietraszyn 49a, Poland, which is dated to the beginning of Marine Isotope Stage 3. Based on the whole data set, it was possible to distinguish four stages of the manufacturing process. During manufacturing, both mineral hammer and organic hammer were used. The tools were usually shaped due to distinct hierarchization of faces. The study has also shown that the shape of bifacial tools from Pietraszyn 49a is very similar to the other Micoquian examples from central Europe. The ways of shaping of some tools are finding their counterparts also in the Early Upper Palaeolithic inventories, but the similarities are rather limited to the narrow range of preparation of bifacial form.

scholarly journals Mining Local Specialties for Travelers by Leveraging Structured and Unstructured Data

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Kai Jiang ◽  
Like Liu ◽  
Rong Xiao ◽  
Nenghai Yu
Keyword(s):  
Daily Life ◽  
Large Data ◽  
Structured Data ◽  
Unstructured Data ◽  
User Generated Content ◽  
Data Set ◽  
Large Cities ◽  
Mining Algorithm ◽  
Large Data Set ◽  
Ranking Score

Recently, many local review websites such as Yelp are emerging, which have greatly facilitated people's daily life such as cuisine hunting. However they failed to meet travelers' demands because travelers are more concerned about a city's local specialties instead of the city's high ranked restaurants. To solve this problem, this paper presents a local specialty mining algorithm, which utilizes both the structured data from local review websites and the unstructured user-generated content (UGC) from community Q&A websites, and travelogues. The proposed algorithm extracts dish names from local review data to build a document for each city, and appliestfidfweighting algorithm on these documents to rank dishes. Dish-city correlations are calculated from unstructured UGC, and combined with thetfidfranking score to discover local specialties. Finally, duplicates in the local specialty mining results are merged. A recommendation service is built to present local specialties to travelers, along with specialties' associated restaurants, Q&A threads, and travelogues. Experiments on a large data set show that the proposed algorithm can achieve a good performance, and compared to using local review data alone, leveraging unstructured UGC can boost the mining performance a lot, especially in large cities.

scholarly journals A Survey on Big Data Analytics Using HADOOP

2019 ◽  
Vol 8 (S3) ◽  
pp. 35-40
Author(s):  
S. Mamatha ◽  
T. Sudha
Keyword(s):  
Big Data ◽  
Social Networking Sites ◽  
Data Analytics ◽  
Business Processes ◽  
Big Data Analytics ◽  
Large Data ◽  
Structured Data ◽  
Map Reduce ◽  
Data Set ◽  
Digital World

In this digital world, as organizations are evolving rapidly with data centric asset the explosion of data and size of the databases have been growing exponentially. Data is generated from different sources like business processes, transactions, social networking sites, web servers, etc. and remains in structured as well as unstructured form. The term ― Big data is used for large data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big data varies in size ranging from a few dozen terabytes to many petabytes of data in a single data set. Difficulties include capture, storage, search, sharing, analytics and visualizing. Big data is available in structured, unstructured and semi-structured data format. Relational database fails to store this multi-structured data. Apache Hadoop is efficient, robust, reliable and scalable framework to store, process, transforms and extracts big data. Hadoop framework is open source and fee software which is available at Apache Software Foundation. In this paper we will present Hadoop, HDFS, Map Reduce and c-means big data algorithm to minimize efforts of big data analysis using Map Reduce code. The objective of this paper is to summarize the state-of-the-art efforts in clinical big data analytics and highlight what might be needed to enhance the outcomes of clinical big data analytics tools and related fields.

scholarly journals Scalable Micro-planned Generation of Discourse from Structured Data

2020 ◽  
Vol 45 (4) ◽  
pp. 737-763 ◽  
Author(s):  
Anirban Laha ◽  
Parag Jain ◽  
Abhijit Mishra ◽  
Karthik Sankaranarayanan
Keyword(s):  
Natural Language ◽  
Structured Data ◽  
Data Sets ◽  
Data Types ◽  
Data Set ◽  
Language Generation ◽  
Parallel Data ◽  
Simple Sentences ◽  
Diverse Data ◽  
Existing Data

We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG). Modern data-to-text NLG systems typically use end-to-end statistical and neural architectures that learn from a limited amount of task-specific labeled data, and therefore exhibit limited scalability, domain-adaptability, and interpretability. Unlike these systems, ours is a modular, pipeline-based approach, and does not require task-specific parallel data. Rather, it relies on monolingual corpora and basic off-the-shelf NLP tools. This makes our system more scalable and easily adaptable to newer domains. Our system utilizes a three-staged pipeline that: (i) converts entries in the structured data to canonical form, (ii) generates simple sentences for each atomic entry in the canonicalized representation, and (iii) combines the sentences to produce a coherent, fluent, and adequate paragraph description through sentence compounding and co-reference replacement modules. Experiments on a benchmark mixed-domain data set curated for paragraph description from tables reveals the superiority of our system over existing data-to-text approaches. We also demonstrate the robustness of our system in accepting other popular data sets covering diverse data types such as knowledge graphs and key-value maps.

scholarly journals Biological data annotation via a human-augmenting AI-based labeling system

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Douwe van der Wal ◽  
Iny Jhun ◽  
Israa Laklouk ◽  
Jeff Nirschl ◽  
Lara Richer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Microscopic Analysis ◽  
Image Data ◽  
Cell Types ◽  
Biological Data ◽  
Data Sets ◽  
Data Set ◽  
Data Annotation ◽  
Labeling System

AbstractBiology has become a prime area for the deployment of deep learning and artificial intelligence (AI), enabled largely by the massive data sets that the field can generate. Key to most AI tasks is the availability of a sufficiently large, labeled data set with which to train AI models. In the context of microscopy, it is easy to generate image data sets containing millions of cells and structures. However, it is challenging to obtain large-scale high-quality annotations for AI models. Here, we present HALS (Human-Augmenting Labeling System), a human-in-the-loop data labeling AI, which begins uninitialized and learns annotations from a human, in real-time. Using a multi-part AI composed of three deep learning models, HALS learns from just a few examples and immediately decreases the workload of the annotator, while increasing the quality of their annotations. Using a highly repetitive use-case—annotating cell types—and running experiments with seven pathologists—experts at the microscopic analysis of biological specimens—we demonstrate a manual work reduction of 90.60%, and an average data-quality boost of 4.34%, measured across four use-cases and two tissue stain types.

scholarly journals Performance Analysis of Machine Learning Algorithms for Big Data Classification

2021 ◽  
Vol 12 (4) ◽  
pp. 60-75
Author(s):  
Sanjeev Kumar Punia ◽  
Manoj Kumar ◽  
Thompson Stephan ◽  
Ganesh Gopal Deverajan ◽  
Rizwan Patan
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Big Data ◽  
Research Data ◽  
Structured Data ◽  
Unstructured Data ◽  
Support Vector ◽  
Classification Algorithms ◽  
Data Set ◽  
Machine Learning Classification

In broad, three machine learning classification algorithms are used to discover correlations, hidden patterns, and other useful information from different data sets known as big data. Today, Twitter, Facebook, Instagram, and many other social media networks are used to collect the unstructured data. The conversion of unstructured data into structured data or meaningful information is a very tedious task. The different machine learning classification algorithms are used to convert unstructured data into structured data. In this paper, the authors first collect the unstructured research data from a frequently used social media network (i.e., Twitter) by using a Twitter application program interface (API) stream. Secondly, they implement different machine classification algorithms (supervised, unsupervised, and reinforcement) like decision trees (DT), neural networks (NN), support vector machines (SVM), naive Bayes (NB), linear regression (LR), and k-nearest neighbor (K-NN) from the collected research data set. The comparison of different machine learning classification algorithms is concluded.

A Method for 3D Structured Data Set Regulation Based on Image

2012 ◽  
pp. 643-648
Author(s):  
Tao He ◽  
Pengcheng Long ◽  
Shaoheng Zhou ◽  
Qin Zeng ◽  
Liqin Hu ◽  
...  
Keyword(s):  
Structured Data ◽  
Data Set
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